PrometheusRoot
Blog Links Prometheans 100+ AI Books AI Companies Why are you here?
← Prometheans 100+
×
Chip Huyen
builder
EducatorAuthor
X / Twitter Website GitHub
ai-engineeringmlopsbooksstanfordproduction-ml

Related

legend Andrew Ng
← Prometheans 100+ Chip Huyen

VP of AI & Open Source at Voltron Data, AI Engineering author

Chip Huyen

VP of AI & Open Source Software — Voltron Data Instructor (former) — Stanford University Staff Engineer (former) — Snorkel AI Senior ML Engineer (former) — NVIDIA
Listen — profile
0:00 / 1:46

Profile

Chip Huyen is the author of two of the most-read books in practical ML and AI today — Designing Machine Learning Systems (2022) and AI Engineering (2025). If you’re trying to go from “I’ve made an LLM return something” to “I run this in production and it doesn’t fall over,” her books are where most people land.

She grew up in Vietnam, studied at Stanford, and stayed on to teach CS 329S: Machine Learning Systems Design — a project-heavy course whose lecture notes became the first book. Before that she built ML tooling at NVIDIA, Netflix, and Snorkel AI. In 2022 she co-founded Claypot AI to do real-time ML infrastructure; in early 2024 Voltron Data acquired the company, and she joined as VP of AI and Open Source, working on GPU-native data processing on top of Apache Arrow and Ibis.

What makes her worth reading is that she refuses to write hype. Her blog posts — the 200-ML-tools landscape, the real-time ML series, the 2025 “common pitfalls” piece — are surveys of what people are actually doing, with opinions attached. She treats evaluation, data, and infrastructure as the hard parts, because they are. For someone learning AI, the shortest path to understanding how real systems get built is to read her cover to cover and then go build something.

She also writes outside tech: four bestselling Vietnamese travel books, starting with Xách Ba Lô Lên Và Đi. The writing voice carries over — plain, observational, no posturing.

Books

AI Engineering: Building Applications with Foundation Models
AI Engineering: Building Applications with Foundation Models
2024 ●
The practical handbook for building products on top of foundation models — evaluation, RAG, fine-tuning, agents, inference optimization, and the rest of the stack.
AI Engineering: Building Applications with Foundation Models

AI Engineering: Building Applications with Foundation Models

Chip Huyen — 2024

Publisher
O'Reilly Media, Incorporated
Pages
350
ISBN
9781098166304
Published
2024
More → Amazon
Designing Machine Learning Systems
Designing Machine Learning Systems
An Iterative Process for Production-Ready Applications
2022 ●
The predecessor and companion to AI Engineering — how to design, deploy, and maintain ML systems that survive contact with production data.
Designing Machine Learning Systems

Designing Machine Learning Systems

An Iterative Process for Production-Ready Applications

Chip Huyen — 2022

Publisher
O'Reilly Media, Incorporated
Pages
386
ISBN
9781098107963
Published
2022
More → Amazon

Key Articles & Papers

AI Engineering (book companion repo) 2025 — Resources, notebooks, and errata for the AI Engineering book — worth following alongside the text. Common pitfalls when building generative AI applications 2025 — Ten mistakes she keeps seeing teams make — from over-indexing on agents to replacing human evaluation with AI judges. Building LLM applications for production 2023 — The foundational post that basically defined the AI engineering stack a year before everyone else was writing about it. Real-time machine learning: challenges and solutions 2022 — A rigorous walkthrough of online prediction, online features, and continual learning — the motivation for Claypot. Machine learning is going real-time 2020 — Why US companies were still doing batch while Chinese companies had moved to online inference and online training. What I learned from looking at 200 machine learning tools 2020 — A landscape survey that made it obvious MLOps was an actual category. Still holds up as a way to think about the space. MLOps guide 2023 — Her curated entry point to MLOps — reading list, definitions, and how the pieces fit together. Open Source LLM Tools (llama-police) 2023 — A live-updated catalog of open-source LLM tooling, built from scraping GitHub stars in the space.

YouTube

YouTube video
2026
YouTube video
2025
YouTube video
2025
YouTube video
2025
YouTube video
2025
YouTube video
2023
YouTube video
2023
YouTube video
2023
YouTube video
2023
YouTube video
2018

Spotify Podcasts

Al Engineering 101 with Chip Huyen (Nvidia, Stanford, Netflix)
Al Engineering 101 with Chip Huyen (Nvidia, Stanford, Netflix)
Lenny's Podcast: Product | Career | Growth
2025
Chip Huyen on Finding Business Use Cases for Generative AI
Chip Huyen on Finding Business Use Cases for Generative AI
Generative AI in the Real World
2025
AI Engineering with Chip Huyen
AI Engineering with Chip Huyen
The Pragmatic Engineer
2025
Chip Huyen - AI Engineering, Agents, and More
Chip Huyen - AI Engineering, Agents, and More
The Joe Reis Show
2025
AI Engineering Pitfalls with Chip Huyen - #715
AI Engineering Pitfalls with Chip Huyen - #715
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
2025
What You MUST Know About AI Engineering in 2025 | Chip Huyen, Author of “AI Engineering”
What You MUST Know About AI Engineering in 2025 | Chip Huyen, Author of “AI Engineering”
The MAD Podcast with Matt Turck
2025
02: Unleashing LLMs in Production: Challenges and Opportunities with Chip Huyen
02: Unleashing LLMs in Production: Challenges and Opportunities with Chip Huyen
Replit AI Podcast
2023
Chip Huyen: Machine Learning Tools and Systems
Chip Huyen: Machine Learning Tools and Systems
The Gradient: Perspectives on AI
2022
11/12/20 #5 Chip Huyen - Principles of Good Machine Learning Systems Design
11/12/20 #5 Chip Huyen - Principles of Good Machine Learning Systems Design
Stanford MLSys Seminar
2022
Chip Huyen — ML Research and Production Pipelines
Chip Huyen — ML Research and Production Pipelines
Gradient Dissent: Conversations on AI
2020

Related People

legend Andrew Ng
© 2026 PrometheusRoot